Probabilistic model to compress images for three-dimensional video

ABSTRACT

A method includes receiving head-tracking data that describe one or more positions of people while the people are viewing a three-dimensional video. The method further includes generating a probabilistic model of the one or more positions of the people based on the head-tracking data, wherein the probabilistic model identifies a probability of a viewer looking in a particular direction as a function of time. The method further includes generating video segments from the three-dimensional video. The method further includes, for each of the video segments: determining a directional encoding format that projects latitudes and longitudes of locations of a surface of a sphere onto locations on a plane, determining a cost function that identifies a region of interest on the plane based on the probabilistic model, and generating optimal segment parameters that minimize a sum-over position for the region of interest.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/617,878, entitled “Probabilistic Model to Compress Images forThree-Dimensional Video,” filed Jun. 8, 2017, which is acontinuation-in-part of U.S. patent application Ser. No. 15/269,734,entitled “Behavioral Directional Encoding of Three-Dimensional Video,”filed Sep. 19, 2016 (now U.S. Pat. No. 9,774,887). This application is acontinuation-in-part of U.S. patent application Ser. No. 14/842,465,entitled “Virtual Reality System Including Social Graph,” filed Sep. 21,2015, which claims priority under 35 USC § 119(e) to U.S. ProvisionalApplication No. 62/142,909, entitled “Image Stitching,” filed Apr. 3,2015 and U.S. Provisional Application No. 62/055,259, entitled “VirtualReality System Including Social Graph,” filed Sep. 25, 2014, is acontinuation-in-part of U.S. patent application Ser. No. 14/726,118,entitled “Camera Array Including Camera Modules,” filed May 29, 2015(now U.S. Pat. No. 9,911,454), and is a continuation-in-part of U.S.application Ser. No. 14/444,938, entitled “Camera Array Including CameraModules,” filed Jul. 28, 2014 (now U.S. Pat. No. 9,451,162), each ofwhich is incorporated by reference.

FIELD

The embodiments discussed herein are related to behavioral directionalencoding of three-dimensional video. More particularly, the embodimentsdiscussed herein relate to using a probabilistic model, such as a heatmap, to determine optimal segment parameters and portions of athree-dimensional video to blur.

BACKGROUND

Generating virtual reality content for a 360° environment may beanalogized to generating content that is displayed in a sphere thatsurrounds a user. Because the user may look anywhere in the sphere,current virtual reality systems generate high-quality content for everypixel in the sphere. As a result, virtual reality content is data rich.Because the user may only look in one direction, most of the pixels inthe view are not seen by the user. For example, it is frequently a wasteof bandwidth to include data rich content that is located behind theuser because the user is unlikely to turn 180 degrees to view thatcontent.

When the virtual reality content is for a video, the data requirementsare massive because the video is generated for each pixel in the sphere.As a result, it may be difficult to stream the virtual reality contentto the user because of bandwidth constraints.

One solution to the problem of current virtual reality systems may be toprovide a viewing device with virtual reality content that correspondsto the direction of the user's gaze. However, because the user may moveand look in a different direction, the movement may result in the userperceiving a lag in the virtual reality content as the virtual realitysystem updates the direction and transmits virtual reality content forthe different direction.

Another solution may be to predict the direction of the user's gaze.However, if the prediction is wrong, the resulting virtual realitycontent may have both lower quality and less stability than traditionalvirtual reality content.

SUMMARY

According to one innovative aspect of the subject matter described inthis disclosure, a method includes receiving head-tracking data thatdescribe one or more positions of people while the people are viewing athree-dimensional video, generating a probabilistic model of the one ormore positions of the people based on the head-tracking data, whereinthe probabilistic model identifies a probability of a viewer looking ina particular direction as a function of time, generating video segmentsfrom the three-dimensional video, and for each of the video segments:determining a directional encoding format that projects latitudes andlongitudes of locations of a surface of a sphere onto locations on aplane, determining a cost function that identifies a region of intereston the plane based on the probabilistic model, and generating optimalsegment parameters that minimize a sum-over position for the region ofinterest.

In some embodiments, the probabilistic model is a heat map. In someembodiments, the method may also include re-encoding thethree-dimensional video to include the optimal segment parameters foreach of the video segments and to blur portions of each of the videosegments based on the probability, wherein an intensity of a level ofblur increases as the probability of the viewer looking in theparticular direction decreases and providing a re-encoded video and theoptimal segment parameters for each of the video segments to a viewingdevice, wherein the viewing device uses the optimal segment parametersfor each of the video segments to un-distort the re-encoded video andtexture the re-encoded video to the sphere to display the re-encodedvideo with the region of interest for each of the video segmentsdisplayed at a higher resolution than other regions in each of the videosegments. In some embodiments, the three-dimensional video may bere-encoded to include the optimal segment parameters for each of thevideo segments and blurring portions of each of the video segmentsoccurs responsive to a threshold number of the people viewing thethree-dimensional video. In some embodiments, the method includes foreach of the video segments, identifying a region of low interest,re-encoding the three-dimensional video to include the optimal segmentparameters for each of the video segments and blurring of the region oflow interest, and providing a re-encoded video and the optimal segmentparameters for each of the video segments to a viewing device, whereinthe viewing device uses the optimal segment parameters for each of thevideo segments to un-distort the re-encoded video and texture there-encoded video to the sphere to display the re-encoded video with theregion of interest for each of the video segments displayed at a higherresolution than other regions in each of the video segments and theregion of low interest displayed at a lower resolution than otherregions in each of the video segments. In some embodiments, the methodincludes re-encoding the three-dimensional video to include the optimalsegment parameters for each of the video segments and blurring portionsof each of the video segments based on the probability, wherein anintensity of a level of blur increases as the probability of the viewerlooking in the particular direction decreases and providing a re-encodedvideo and the optimal segment parameters for each of the video segmentsto a client device, wherein the client device uses the re-encoded videoand the optimal segment parameters for each of the video segments togenerate a two-dimensional video that automates head movement. Themethod may further include providing a user with an option to modify thetwo-dimensional video by at least one of selecting different optimalsegment parameters and selecting a different region of interest for oneor more of the video segments. In some embodiments, the method furtherincludes cropping the region of interest for one or more video segmentsbased on the optimal segment parameters to form one or more thumbnailsof one or more cropped regions of interest and generating a timeline ofthe three-dimensional video with the one or more thumbnails. In someembodiments, generating the video segments from the three-dimensionalvideo includes generating equal-length video segments of a predeterminedlength.

In some embodiments, a system comprises one or more processors coupledto a memory, a head tracking module stored in the memory and executableby the one or more processors, the head tracking module operable toreceive head-tracking data that describe one or more positions of peoplewhile the people are viewing a set of three-dimensional videos, generatea set of probabilistic models of the one or more positions of the peoplebased on the head-tracking data, and estimate a first probabilisticmodel for a first three-dimensional video, wherein the firstprobabilistic model identifies a probability of a viewer looking in aparticular direction as a function of time and the firstthree-dimensional video is not part of the set of three-dimensionalvideos, a segmentation module stored in the memory and executable by theone or more processors, the segmentation module operable to generatevideo segments from the three-dimensional video, and a parameterizationmodule stored in the memory and executable by the one or moreprocessors, the parameterization module operable to, for each of thevideo segments: determine a directional encoding format that projectslatitudes and longitudes of locations of a surface of a sphere ontolocations on a plane, determine a cost function that identifies a regionof interest on the plane based on the first probabilistic model, andgenerate optimal segment parameters that minimize a sum-over positionfor the region of interest.

Other aspects include corresponding methods, systems, apparatus, andcomputer program products for these and other innovative aspects.

The disclosure is particularly advantageous in a number of respects.First, the virtual reality application generates a probabilistic model,such as a heat map, that describes a probability of a viewer looking ina particular direction as a function of time. The virtual realityapplication may re-encode a three-dimensional video based on theprobabilistic model to blur portions of the three-dimensional video andto optimize regions of interest in the three-dimensional video based onthe probability. As a result, the re-encoded three-dimensional video maybe transmitted to a client device with a lower bitrate and may beperceived by a viewer as having higher visual quality than otherexamples of three-dimensional video. In addition, less bandwidth isspent by the client device to un-distort and re-encode thethree-dimensional video with blurred portions.

The client device includes a codec that is advantageously compatiblewith the methods described in this application. As a result, noadditional software is needed for the client device to render thethree-dimensional video. In addition, because the blurring occurslocally and reduces the entropy of that region of the three-dimensionalvideo, it makes the three-dimensional video more compressive. As aresult, the codec on the client device spends less bandwidth on theblurred portions relative to the rest of the three-dimensional video andmore bandwidth on the regions of interest in the three-dimensionalvideo.

In some embodiments, the virtual reality application generatesprobabilistic models for a set of three-dimensional videos and usesartificial intelligence, such as a neural network, to estimate aprobabilistic model for a particular three-dimensional video. Thisadvantageously avoids the need to find people to view everythree-dimensional video in order to generate head-tracking data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example virtual reality system that generatesoptimal segment parameters for a three-dimensional video according tosome embodiments.

FIG. 2 illustrates an example computing device that generates optimalsegment parameters for a three-dimensional video according to someembodiments.

FIG. 3 illustrates an example user interface that includes a timeline ofa video with thumbnails according to some embodiments.

FIG. 4 illustrates an example flow diagram for generating optimalsegment parameters for a three-dimensional video according to someembodiments.

FIG. 5 illustrates an example flow diagram for re-encoding athree-dimensional video with blurred portions.

FIG. 6 illustrates an example flow diagram for generating optimalsegment parameters and a probabilistic model from a training set.

DESCRIPTION OF EMBODIMENTS

The disclosure relates to generating virtual reality content. A virtualreality application receives head-tracking data that describe positionsof people's heads while the people are viewing a three-dimensionalvideo. For example, the head-tracking data measures the yaw, pitch, androll associated with people that are using a viewing device to view thethree-dimensional video.

The virtual reality application generates probabilistic model of the oneor more positions of the people based on the head-tracking data. Theprobabilistic model identifies a probability of a viewer looking in aparticular direction as a function of time. For example, if most peoplethat view the video look straight ahead at a particular object, thedirection that is directly behind most people is unlikely to be adirection that people look in.

The virtual reality application generates video segments from thethree-dimensional video. For example, the video segments may be a fixedlength of time, such as two seconds (or three, four, etc.) or the videosegments may be based on scene boundaries in the three-dimensionalvideo.

For each of the video segments, the virtual reality applicationdetermines a directional encoding format that projects latitudes andlongitudes of locations of a surface of sphere onto locations on aplane. For example, the virtual reality application may use a mapprojection to take three-dimensional video content that is designed fora sphere and map it onto a plane. For each of the video segments, thevirtual reality application determines a cost function that identifies aregion of interest on the plane based on the probabilistic model. Forexample, the virtual reality application may determine that most peoplelook in a particular direction during the video segment. For each of thevideo segments, the virtual reality application generates optimalsegment parameters that minimize a sum-over position for the region ofinterest. For example, the virtual reality application generates yaw,pitch, and roll values for the segment to identify the region ofinterest.

The optimal segment parameters may be used in a variety of applications.For example, the virtual reality application may re-encode thethree-dimensional video to include the optimal segment parameters foreach of the video segments, blur portions of each of the video segmentsbased on the probability, and provide the re-encoded video and theoptimal segment parameters to a viewing device. The viewing device mayuse the optimal segment parameters to un-distort the re-encoded videoand texture the re-encoded video to the sphere. As a result, a userusing the viewing the three-dimensional video may view the regions ofinterest at a higher resolution than the other regions in thethree-dimensional video and the blurred portions of thethree-dimensional video at a lower resolution than other regions in thethree-dimensional video.

Example System

FIG. 1 illustrates an example virtual reality system 100 that determinesoptimal segment parameters for virtual reality content. The virtualreality system 100 comprises a camera array 101, a client device 105, aviewing device 115, a server 120, and a network 125.

While FIG. 1 illustrates one camera array 101, one client device 105,one viewing device 115, and one server 120, the disclosure applies to asystem architecture having one or more camera arrays 101, one or moreclient devices 105, one or more viewing devices 115, and one or moreservers 120. Furthermore, although FIG. 1 illustrates one network 125coupled to the entities of the system 100, in practice one or morenetworks 125 may be connected to these entities and the one or morenetworks 125 may be of various and different types.

The camera array 101 may comprise camera modules that capture videodata. The camera array 101 may communicate with the client device 105and/or the server 120 by accessing the network 125 via signal line 102.Signal line 102 may represent a wireless or a wired connection. Forexample, the camera array 101 may wirelessly transmit video data overthe network 125 to the server 120. In some embodiments, the camera array101 may be directly connected to the client device 105. For example, thecamera array 101 may be connected to the client device 105 via auniversal serial bus (USB) cable.

The network 125 may be a conventional type, wired or wireless, and mayhave numerous different configurations including a star configuration,token ring configuration, or other configurations. Furthermore, thenetwork 125 may include a local area network (LAN), a wide area network(WAN) (e.g., the Internet), or other interconnected data paths acrosswhich multiple devices may communicate. In some embodiments, the network125 may be a peer-to-peer network. The network 125 may also be coupledto or include portions of a telecommunications network for sending datain a variety of different communication protocols. In some embodiments,the network 125 may include Bluetooth™ communication networks or acellular communication network for sending and receiving data includingvia short messaging service (SMS), multimedia messaging service (MMS),hypertext transfer protocol (HTTP), direct data connection, wirelessaccess protocol (WAP), e-mail, etc.

The client device 105 may be a processor-based computing device. Forexample, the client device 105 may be a personal computer, laptop,tablet computing device, smartphone, set top box, network-enabledtelevision, or any other processor based computing device. In someembodiments, the client device 105 includes network functionality and iscommunicatively coupled to the network 125 via a signal line 104. Theclient device 105 may be configured to transmit data to the server 120or to receive data from the server 120 via the network 125. A user 110may access the client device 105.

The client device 105 may include a virtual reality (VR) application 103a. The virtual reality application 103 a may be configured to controlthe camera array 101 and/or aggregate video data and audio data togenerate a stream of three-dimensional video data. In some embodiments,the virtual reality application 103 a can be implemented using hardwareincluding a field-programmable gate array (“FPGA”) or anapplication-specific integrated circuit (“ASIC”). In some otherembodiments, the virtual reality application 103 a may be implementedusing a combination of hardware and software.

The server 120 may be a hardware server that includes a processor, amemory, a database 107, and network communication capabilities. In theillustrated embodiment, the server 120 is coupled to the network 125 viasignal line 108. The server 120 sends and receives data to and from oneor more of the other entities of the system 100 via the network 125. Forexample, the server 120 receives virtual reality content including astream of three-dimensional video data (or compressed three-dimensionalvideo data) from the camera array 101 and/or the client device 105 andstores the virtual reality content on a storage device (e.g., thedatabase 107) associated with the server 120. The database 107 may storea set of three-dimensional videos that are used to generatehead-tracking data. For example, the virtual reality application 103 bmay use the head-tracking data to estimate a probabilistic model for athree-dimensional video that is not part of the set of three-dimensionalvideos.

The server 120 may include a virtual reality application 103 b thatreceives video data and audio data from the client device 105 and/or thecamera array 101 and aggregates the video data to generate the virtualreality content. The virtual reality application 103 b may generate theoptimal segment parameters for the three-dimensional video.

The viewing device 115 may be operable to display virtual realitycontent. The viewing device 115 may include or use a computing device todecode and render a stream of three-dimensional video data on a virtualreality display device (e.g., Oculus Rift virtual reality display) orother suitable display devices that include, but are not limited to:augmented reality glasses; televisions, smartphones, tablets, or otherdevices with three-dimensional displays and/or position trackingsensors; and display devices with a viewing position control, etc. Theviewing device 115 may also decode and render a stream ofthree-dimensional audio data on an audio reproduction device (e.g., aheadphone or other suitable speaker devices). The viewing device 115 mayinclude the virtual reality display configured to render thethree-dimensional video data and the audio reproduction deviceconfigured to render the three-dimensional audio data.

The viewing device 115 may be coupled to the network 125 via signal line106. The viewing device 115 may communicate with the client device 105and/or the server 120 via the network 125 or via a direct connectionwith the client device 105 (not shown). A user 113 may interact with theviewing device 115. The user 113 may be the same or different from theuser 110 that accesses the client device 105.

In some embodiments, the viewing device 115 receives virtual realitycontent from the client device 105. Alternatively or additionally, theviewing device 115 receives the virtual reality content from the server120. The virtual reality content may include one or more of a stream ofthree-dimensional video data, a stream of three-dimensional audio data,a compressed stream of three-dimensional video data, a compressed streamof three-dimensional audio data, and other suitable content. In someembodiments, the viewing device 115 and the client device 105 may be thesame device.

The viewing device 115 may track a head orientation of a user 113 whilethe user 113 is viewing three-dimensional video. For example, theviewing device 115 may include one or more accelerometers or gyroscopesused to detect a change in the user's 113 head orientation. The viewingdevice 115 may decode and render the stream of three-dimensional videodata on a virtual reality display device based on the head orientationof the user 113. As the user 113 changes his or her head orientation,the viewing device 115 may adjust the rendering of the three-dimensionalvideo data and three-dimensional audio data based on the changes of theuser's 113 head orientation. The viewing device 115 may loghead-tracking data and transmit the head-tracking data to the virtualreality application 103. Although not illustrated, in some embodimentsthe viewing device 115 may include some or all of the components of thevirtual reality application 103 described below.

The virtual reality application 103 may receive the head-tracking datacorresponding to the three-dimensional video from the viewing device115. The virtual reality application 103 may generate video segmentsfrom the three-dimensional video and determine optimal segmentparameters for each of the video segments based on the head-trackingdata. For example, the virtual reality application 103 may receivehead-tracking data for multiple users 113 and determine from thehead-tracking data that most users 113 have particular head orientationsduring the viewing. The particular head orientations could includelooking upwards as a bird is displayed as flying overhead, moving fromleft to right as a car is displayed as driving past the user 113, etc.The virtual reality application 103 may transmit the optimal segmentparameters to the viewing device 115, which may use the optimal segmentparameters to re-encode the three-dimensional video. For example, theviewing device 115 may re-encode the three-dimensional video to includeregions of interest (i.e., one or more areas where users 113 were morelikely to look) with a higher resolution than other regions of thethree-dimensional video.

Example Computing Device

FIG. 2 illustrates an example computing device 200 that generatesthree-dimensional video according to some embodiments. The computingdevice 200 may be the server 120 or the client device 105. In someembodiments, the computing device 200 may include a special-purposecomputing device configured to provide some or all of the functionalitydescribed below with reference to FIG. 2.

FIG. 2 may include a processor 222, a memory 224, a communication unit226, and a display 228. The processor 222, the memory 224, thecommunication unit 226, and the display 228 are communicatively coupledto the bus 220. Other hardware components may be part of the computingdevice 200, such as sensors (e.g., a gyroscope, accelerometer), etc.

The processor 222 may include an arithmetic logic unit, amicroprocessor, a general-purpose controller, or some other processorarray to perform computations and provide electronic display signals toa display device. The processor 222 processes data signals and mayinclude various computing architectures including a complex instructionset computer (CISC) architecture, a reduced instruction set computer(RISC) architecture, or an architecture implementing a combination ofinstruction sets. Although FIG. 2 includes a single processor 222,multiple processors may be included. Other processors, operatingsystems, sensors, displays, and physical configurations may be possible.The processor 222 is coupled to the bus 220 for communication with theother components via signal line 203.

The memory 224 stores instructions or data that may be executed by theprocessor 222. The instructions or data may include code for performingthe techniques described herein. For example, the memory 224 may storethe virtual reality application 103, which may be a series of modulesthat include instructions or data for generating three-dimensionalvideos.

The memory 224 may include a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory, or some othermemory device. In some embodiments, the memory 224 also includes anon-volatile memory or similar permanent storage device and mediaincluding a hard disk drive, a CD-ROM device, a DVD-ROM device, aDVD-RAM device, a DVD-RW device, a flash memory device, or some othermass storage device for storing information on a more permanent basis.The memory 224 is coupled to the bus 220 for communication with theother components via signal line 205.

The communication unit 226 may include hardware that transmits andreceives data to and from the camera array 101, the viewing device 115,and the client device 105 or the server 120, depending on whether thevirtual reality application 103 is stored on the server 120 or theclient device 105, respectively. The communication unit 226 is coupledto the bus 220 via signal line 207. In some embodiments, thecommunication unit 226 includes one or more ports for direct physicalconnection to the network 125 or another communication channel. Forexample, the communication unit 226 includes a USB, SD, CAT-5, orsimilar port for wired communication with the computing device 200. Insome embodiments, the communication unit 226 includes a wirelesstransceiver for exchanging data with the computing device 200 or othercommunication channels using one or more wireless communication methods,including IEEE 802.11, IEEE 802.16, Bluetooth®, or another suitablewireless communication method.

In some embodiments, the communication unit 226 includes a cellularcommunications transceiver for sending and receiving data over acellular communications network including via short messaging service(SMS), multimedia messaging service (MMS), hypertext transfer protocol(HTTP), direct data connection, WAP, e-mail, or another suitable type ofelectronic communication. In some embodiments, the communication unit226 includes a wired port and a wireless transceiver. The communicationunit 226 also provides other conventional connections to the network 125for distribution of files or media objects using standard networkprotocols including TCP/IP, HTTP, HTTPS, and SMTP, etc.

The display 228 may include hardware for displaying graphical data fromthe virtual reality application 103. For example, the display 228renders graphics for displaying a user interface where a user may view atwo-dimensional video that was generated from a three-dimensional video.The display 228 is coupled to the bus 220 via signal line 209. Thedisplay 228 is optional hardware that may not be included in thecomputing device 200, for example, if the computing device 200 is aserver.

The virtual reality application 103 may include an aggregation module202, a head tracking module 204, a segmentation module 206, aparameterization module 208, an encoder module 210, and a user interfacemodule 212. Although the modules are illustrated as being part of thesame computing device 200, in some embodiments some of the modules arestored on the server 120 and some of the modules are stored on theclient device 105. For example, the server 120 may include the headtracking module 204, the segmentation module 206, the parameterizationmodule, and the encoder module 210 and the client device 105 may includethe user interface module 212.

The aggregation module 202 may include code and routines for aggregatingvideo data. In some embodiments, the aggregation module 202 includes aset of instructions executable by the processor 222 to aggregate videodata. In some embodiments, the aggregation module 202 is stored in thememory 224 of the computing device 200 and is accessible and executableby the processor 222. In some embodiments, the aggregation module 202may be part of a separate application.

The aggregation module 202 may receive video data from the camera array101. In some embodiments, the video data includes separate videorecordings for each camera module included in the camera array 101 and adevice identifier (ID) that identifies the camera module correspondingto each separate video recording.

A two-dimensional (2D) spherical panoramic image may be used torepresent a panorama of an entire scene. The aggregation module 202 maygenerate two stereoscopic panorama images for two eyes to provide astereoscopic view of the entire scene. For example, a left panoramicimage may be generated for the left eye viewing and a right panoramicimage may be generated for the right eye viewing.

A pixel in a panoramic image may be represented by a yaw value and apitch value. Yaw represents rotation around the center and may berepresented on the horizontal x-axis as: yaw=360°×x/width. Yaw has avalue between 0° and 360°. Pitch represents up or down rotation and maybe represented on the vertical y-axis as:pitch=90°×(height/2−y)/(height/2). Pitch has a value between −90° and90°.

Typical stereoscopic systems (e.g., three-dimensional movies) mayrespectively show two different planar images to two eyes to create asense of depth. In each planar image, all pixels in the image representa single eye viewing position. For example, all pixels in the planarimage may represent a view into the same viewing direction. However, inthe panoramic image described herein (the left or right panoramicimage), each pixel in the panoramic image may represent a view into aslightly different direction. For example, a pixel at an x position withpitch=0° in a left panoramic image may represent an eye viewing positionof the left eye as the head is rotated by the yaw indicated by the xposition. Similarly, a pixel at an x position with pitch=0° in a rightpanoramic image represents an eye viewing position of the right eye asthe head is rotated by the yaw indicated by the x position. For pitch=0°(e.g., no up and down rotations), as the head is rotated from x=0 tox=width, a blended panorama for eye viewing positions with all360-degree head rotations in the horizontal axis may be produced.

In some implementations, the blended panorama is effective for headrotations along the horizontal axis (e.g., yaw) but not for the verticalaxis (e.g., pitch). For example, when a user looks upward, the qualityof the stereo view may degrade. In order to correct this deficiency, theinterocular distance may be adjusted based on the current pitch value.For example, if pitch≠0°, the interocular distance associated with thepitch may be adjusted as: interocular distance=max(interoculardistance)×cos(pitch), where max(interocular distance) represents themaximum value of the interocular distance (e.g., the interoculardistance is at its maximum when pitch=0°). In some examples, the maximumvalue of the interocular distance may be about 60 millimeters. In otherexamples, the maximum value of the interocular distance may have a valuegreater than 60 millimeters or less than 60 millimeters.

The aggregation module 202 may construct a left camera mapping map foreach pixel in a left panoramic image. For example, for a pixel in a leftpanoramic image that represents a point in a panorama, the left cameramapping map may identify matching camera modules from a camera arraywith spherical modules that have each a better view for the point in thepanorama than other camera modules. Thus, the left camera mapping mapmay map pixels in a left panoramic image to matching camera modules thathave better views for the corresponding pixels.

For each pixel in a left panoramic image that represents a point in apanorama, the aggregation module 202 may determine a yaw, a pitch, andan interocular distance using the above mathematical expressions (1),(2), and (3), respectively. The aggregation module 202 may use the yawand pitch to construct a vector representing a viewing direction of theleft eye (e.g., a left viewing direction) to the corresponding point inthe panorama.

Similarly, the aggregation module 202 may construct a right cameramapping map that identifies a corresponding matching camera module foreach pixel in a right panoramic image. For example, for a pixel in aright panoramic image that represents a point in a panorama, the rightcamera mapping map may identify a matching camera module that has abetter view for the point in the panorama than other camera modules.Thus, the right camera mapping map may map pixels in a right panoramicimage to matching camera modules that have better views for thecorresponding pixels.

For each pixel in a right panoramic image that represents a point in apanorama, the aggregation module 202 may determine a yaw, a pitch, andan interocular distance using the above mathematical expressions,respectively. The aggregation module 202 may use the yaw and pitch toconstruct a vector representing a viewing direction of the right eye(e.g., a right viewing direction) to the corresponding point in thepanorama.

The aggregation module 202 may receive video recordings that describeimage frames from the various camera modules in a camera array. Theaggregation module 202 identifies a location and timing associated witheach of the camera modules and synchronizes the image frames based onlocations and timings of the camera modules. The aggregation module 202synchronizes image frames captured by different camera modules at thesame time frames.

For example, the aggregation module 202 receives a first video recordingwith first images from a first camera module and a second videorecording with second images from a second camera module. Theaggregation module 202 identifies that the first camera module islocated at a position with yaw=0° and pitch=0° and the second cameramodule is located at a position with yaw=30° and pitch=0°. Theaggregation module 202 synchronizes the first images with the secondimages by associating a first image frame from the first images at atime frame T=T₀ with a second image frame from the second images at thetime frame T=T₀, a third image frame from the first images at a timeframe T=T₁ with a fourth image frame from the second images at the timeframe T=T₁, and so on and so forth.

The aggregation module 202 may construct a stream of left panoramicimages from the image frames based on the left camera mapping map. Forexample, the aggregation module 202 identifies matching camera moduleslisted in the left camera mapping map. The aggregation module 202constructs a first left panoramic image PI_(L,0) for a first time frameT=T₀ by stitching together image frames captured at the first time frameT=T₀ by the matching camera modules. The aggregation module 202constructs a second left panoramic image PI_(L,1) at a second time frameT=T₁ using image frames captured at the second time frame T=T₁ by thematching camera modules, and so on and so forth. The aggregation module202 constructs the stream of left panoramic images to include the firstleft panoramic image PI_(L,0) at the first time frame T=T₀, the secondleft panoramic image PI_(L,1) at the second time frame T=T₁, and otherleft panoramic images at other corresponding time frames.

Specifically, for a pixel in a left panoramic image PI_(L,i) at aparticular time frame T=T₁ (i=0, 1, 2, . . . ), the aggregation module202: (1) identifies a matching camera module from the left cameramapping map; and (2) configures the pixel in the left panoramic imagePI_(L,i) to be a corresponding pixel from an image frame captured by thematching camera module at the same time frame T=T₁. The pixel in theleft panoramic image PI_(L,i) and the corresponding pixel in the imageframe of the matching camera module may correspond to the same point inthe panorama. For example, for a pixel location in the left panoramicimage PI_(L,i) that corresponds to a point in the panorama, theaggregation module 202: (1) retrieves a pixel that also corresponds tothe same point in the panorama from the image frame captured by thematching camera module at the same time frame T=T₁; and (2) places thepixel from the image frame of the matching camera module into the pixellocation of the left panoramic image PI_(L,i).

Similarly, the aggregation module 202 constructs a stream of rightpanoramic images from the image frames based on the right camera mappingmap by performing operations similar to those described above withreference to the construction of the stream of left panoramic images.For example, the aggregation module 202 identifies matching cameramodules listed in the right camera mapping map. The aggregation module202 constructs a first right panoramic image PI_(R,0) for a first timeframe T=T₀ by stitching together image frames captured at the first timeframe T=T₀ by the matching camera modules. The aggregation module 202constructs a second right panoramic image PI_(R,1) at a second timeframe T=T₁ using image frames captured at the second time frame T=T₁ bythe matching camera modules, and so on and so forth. The aggregationmodule 202 constructs the stream of right panoramic images to includethe first right panoramic image PI_(R,0) at the first time frame T=T₀,the second right panoramic image PI_(R,1) at the second time frame T=T₁,and other right panoramic images at other corresponding time frames.

Specifically, for a pixel in a right panoramic image PI_(R,i) at aparticular time frame T=T₁ (i=0, 1, 2, . . . ), the aggregation module202: (1) identifies a matching camera module from the right cameramapping map; and (2) configures the pixel in the right panoramic imagePI_(R,i) to be a corresponding pixel from an image frame captured by thematching camera module at the same time frame T=T₁. The pixel in theright panoramic image PI_(R,i) and the corresponding pixel in the imageframe of the matching camera module may correspond to the same point inthe panorama.

The aggregation module 202 may obtain virtual reality content from thestream of left panoramic images, the stream of right panoramic images,and the audio data by sending one or more of the stream of leftpanoramic images, the stream of right panoramic images, and the audiodata to the encoder module 210 for encoding. The encoder module 210 maycompress the stream of left panoramic images and the stream of rightpanoramic images to generate a stream of compressed three-dimensionalvideo data using video compression techniques. In some implementations,within each stream of the left or right panoramic images, the encodermodule 210 may use redundant information from one frame to a next frameto reduce the size of the corresponding stream. For example, withreference to a first image frame (e.g., a reference frame), redundantinformation in the next image frames may be removed to reduce the sizeof the next image frames. This compression may be referred to astemporal or inter-frame compression within the same stream of left orright panoramic images.

Alternatively or additionally, the encoder module 210 may use one stream(either the stream of left panoramic images or the stream of rightpanoramic images) as a reference stream and may compress the otherstream based on the reference stream. This compression may be referredto as inter-stream compression. For example, the encoder module 210 mayuse each left panoramic image as a reference frame for a correspondingright panoramic image and may compress the corresponding right panoramicimage based on the referenced left panoramic image. The encoding processis discussed in greater detail below with reference to the encodermodule 210. Once the encoder module 210 completes the encoding process,the aggregation module 202 may transmit, via the communication unit 226,the three-dimensional video to the viewing device 115.

The head tracking module 204 may include code and routines for receivinghead tracking data and generating a probabilistic model. In someembodiments, the head tracking module 204 includes a set of instructionsexecutable by the processor 222 to receive head tracking data andgenerate the probabilistic model. In some embodiments, the head trackingmodule 204 is stored in the memory 224 of the computing device 200 andis accessible and executable by the processor 222.

The head tracking module 204 may receive head tracking data from theviewing device 115 that corresponds to a three-dimensional video. Thehead tracking data may describe a person's head movement as the personwatches the three-dimensional video. For example, the head tracking datamay reflect that a person moved her head up and to the right to look atan image of a squirrel in a tree. In some embodiments, the head trackingdata includes yaw (i.e., rotation around a vertical axis), pitch (i.e.,rotation around a side-to-side axis), and roll (i.e., rotation around afront-to-back axis) for a person as a function of time that correspondsto the three-dimensional video. In some implementations, the headtracking module 204 determines a head-mounted display position for eachperson at a particular frequency, such as 10 Hz throughout thethree-dimensional video.

In some embodiments, the head tracking module 204 generates userprofiles based on the head tracking data. For example, the head trackingmodule 204 may aggregate head tracking data from multiple people andorganize it according to a first most common region of interest in thethree-dimensional video, a second most common region of interest in thethree-dimensional video, and a third most common region of interest inthe three-dimensional video. In some embodiments, the head trackingmodule 204 may generate user profiles based on demographic informationcorresponding to the people. For example, the head tracking module 204may generate a user profile based on age, gender, etc. In someembodiments, the head tracking module 204 may generate a user profilebased on physical characteristics. For example, the head tracking module204 may identify people that move frequently while viewing thethree-dimensional video and people that move very little. In someembodiments, the head tracking module 204 generates a user profile for aparticular user.

The head tracking module 204 generates a probabilistic model of one ormore positions of people that view a three-dimensional video. Theprobabilistic model identifies a probability of a viewer looking in aparticular direction as a function of time. For example, theprobabilistic model identifies that a viewer will likely look at aparticular object as it moves in the three-dimensional video and thatthe viewer is unlikely to look direction behind the current locationwhere the viewer is looking.

The head tracking module 204 may generate the probabilistic model on apixel-by-pixel basis, based on regions in the view, such as afield-of-view, equal-sized divisions of the sphere, etc.

The probabilistic model may include a heat map. For example, the heatmap may be rendered as a sequence of false-colored images. In someembodiments, the probabilistic model is displayed as an overlay on topof the three-dimensional video. In some embodiments, the probabilisticmodel is not displayed but is instead used by the encoder module 210 asdescribed below.

In some embodiments, the parameterization module 208 uses theprobabilistic model to determine where one or more people are looking.For example, analysis of one or more probabilistic models may indicatethat people frequently look in particular direction when watching agiven piece of virtual reality content. Subsequent people may benefitfrom this information since it may help them to know where they shouldbe looking when watching the virtual reality content. The encoder module210 may present recommendations to people about where they should belooking when viewing virtual reality content. The recommendations may beaudio cues, visual cues or a combination of audio and visual cues. Insome embodiments, the visual cues may include blurring every portion ofthe virtual reality content except for the recommended location where aviewer should be looking.

In some embodiments, the head tracking module 204 may use artificialintelligence to generate a set of probabilistic models from a set ofthree-dimensional videos. For example, the database 107 stored on theserver 120 may include all three-dimensional videos offered by a companythat generates virtual reality content. The head tracking module 204 mayuse head-tracking data from users that view those three-dimensionalvideos as a training set for generating the set of probabilistic models.The head tracking module 204 may include a neural network that istrained using the set of probabilistic models to determine aprobabilistic distribution of viewer gaze.

In some embodiments, the artificial intelligence may be usediteratively, such that each time a new three-dimensional video isgenerated, the head tracking module 204 uses artificial intelligence(e.g., the neural network) to generate a probabilistic model for the newthree-dimensional video. This advantageously results in the creation ofprobabilistic models for three-dimensional videos that have never beenwatched.

The segmentation module 206 may include code and routines for generatingvideo segments from the three-dimensional video. In some embodiments,the segmentation module 206 includes a set of instructions executable bythe processor 222 to generate the video segments. In some embodiments,the segmentation module 206 is stored in the memory 224 of the computingdevice 200 and is accessible and executable by the processor 222.

The segmentation module 206 generates video segments from thethree-dimensional video. In some embodiments, the segmentation module206 generates equal-length video segments of a predetermined length. Forexample, the segmentation module 206 divides a three-minutethree-dimensional video into 360 two-second segments. In someembodiments, the segmentation module 206 detects scene boundaries in thethree-dimensional video and segments the three-dimensional video basedon the scene boundaries. For example, the segmentation module 206compares a first frame to a next frame to identify differences thatindicate a transition between shots. When the segmentation module 206detects the transition between shots, the segmentation module 206generates a segment that includes the shot. In some embodiments, thesegmentation module 206 may generate segments using a combination ofdetection of scene boundaries and timing. For example, the segmentationmodule 206 may first segment the three-dimensional video based ontransitions between shots and further segment if any shots exceed apredetermined length of time, such as five seconds.

The parameterization module 208 may include code and routines forgenerating optimal segment parameters. In some embodiments, theparameterization module 208 includes a set of instructions executable bythe processor 222 to generate the optimal segment parameters. In someembodiments, the parameterization module 208 is stored in the memory 224of the computing device 200 and is accessible and executable by theprocessor 222.

Three-dimensional video is viewable in all directions. Thus, thethree-dimensional video may be modeled by a sphere where a user is inthe center of the sphere and may view content from the three-dimensionalvideo in any direction. In some embodiments, the parameterization module208 converts the locations on the surface of the sphere into a plane.For example, the parameterization module 208 may use a map projection totransform the latitudes and longitudes of locations on the surface ofthe sphere into locations on a plane. In some embodiments, for each ofthe video segments, the parameterization module 208 determines adirectional encoding format (i.e., a map projection) that projectslatitudes and longitudes of locations of the surface of the sphere intolocations on the plane. The directional encoding format, i.e., theprojection of the latitudes and longitudes of locations of the surfaceof the sphere may be represented by the following equation:f(yaw,pitch,roll,parameters)→resolution  Eq. (1a)

where the yaw, pitch, and roll values are obtained from thehead-tracking data and/or the probabilistic model. Specifically, theyaw, pitch, and roll values describes a position of a person that isviewing the three-dimensional video as a function of time. The yaw,pitch, and roll values may include head-tracking data that is aggregatedfor multiple people that view the three-dimensional video. Theparameters represent a location in the plane and the resolution is theresolution of the three-dimensional video at a region that correspondsto the yaw, pitch, and roll values.

In some embodiments, the directional encoding format may be representedby the following equation:f(parameters(pitch,yaw))→resolution  Eq. (1b)

The parameterization module 208 may design a cost function that gives ameasure of perceived resolution (e.g., a geometric mean of horizontaland vertical pixels per degree at a display center) for a user gazing ina particular direction at a particular timestamp for a particular set ofparameters for the projection. For example, where the latitude/longitudeis 0 on the sphere, the particular set of parameters may indicate howbiased the encoding is towards its high-resolution region. In someembodiments, the total cost function may be defined as a sum of theindividual costs as a function of optimal segment parameters at aparticular point in the three-dimensional video.

The parameterization module 208 may set a resolution threshold, such as10 pixels per degree, that is display and bandwidth-target dependent. Iff(parameters) is greater than the resolution threshold, there is nobenefit and a cost function that incorporates hinge loss from machinelearning may be represented by the following equation:cost(yaw,pitch,roll,params)=max(10−f(yaw,pitch,roll,params),0)  Eq. (2a)

where params represents the optimal segment parameters. Theparameterization module 208 uses the cost function to identify a regionof interest on the plane based on the head-tracking data and/or theprobabilistic model by minimizing a total cost for all users that viewedthe three-dimensional video. Persons of ordinary skill in the art willrecognize that other cost functions may be used. The parameterizationmodule 208 may generate optimal segment parameters that minimize asum-over position for the region of interest by applying the costfunction. The optimal segment parameters may include a (yaw, pitch)tuple that encodes the region of interest in the video segment. In someembodiments, the parameterization module 208 determines one or moreregions of low interest based on the probabilistic model. For example, aregion of low interest may include a field-of-view or other division ofa three-dimensional video based on the probabilistic model.

In some embodiments, the parameterization module 208 determines multipledirectional encodings in each of the video segments forthree-dimensional video to identify multiple regions of interest withinthe three-dimensional video. For example, the head tracking module 204generates a first user profile and a second user profile and theparameterization module 208 generates first optimal segment parametersassociated with the first user profile and second optimal segmentparameters associated with the second user profile.

The parameterization module 208 may determine the multiple directionalencodings using time-dependent clustering and/or a model that is similarto k-means clustering. The parameterization module 208 may determine npaths in the three-dimensional video where each path represents anindependent set of parameters. If n>1, the cost function may be definedas:cost_multi(yaw,pitch,roll,parameter_sets)=max([cost(yaw,pitch,roll,param_set)for param_set in parameter_sets])  Eq. (2b)

In some embodiments, a new directional encoding format may be designedwith multiple potential regions of interest. The new directionalencoding format may be converted into the above resolution and costfunctions.

The encoder module 210 may include code and routines for re-encoding thethree-dimensional video. In some embodiments, the encoder module 210includes a set of instructions executable by the processor 222 tore-encode the three-dimensional video. In some embodiments, the encodermodule 210 is stored in the memory 224 of the computing device 200 andis accessible and executable by the processor 222.

The encoder module 210 may re-encode the three-dimensional video toinclude the optimal segment parameters for each of the video segments.For example, the encoder module 210 may re-encode the three-dimensionalvideo by generating a re-encoded video that includes a high-resolutionversion of the region of interest and a lower resolution version of theother regions in the re-encoded video. The encoder module 210 maytransmit, via the communication unit 226, the re-encoded video and theoptimal segment parameters for each of the video segments to the viewingdevice 115.

In some embodiments, the encoder module 210 re-encodes thethree-dimensional video by blurring portions of the three-dimensionalvideo. The encoder module 210 may blur on a pixel-by-pixel basisaccording to a probability that the viewer is looking at a particularpixel based on the probabilistic model. Alternatively or additionally,the encoder module 210 may blur based on regions of interest or regionsof low interest.

In some embodiments, the encoder module 210 blurs each of the videosegments with varying intensity such that the intensity of a level ofblur increases as the probability of a viewer looking in a particulardirection decreases. For example, a video segment with a single movingobject may include the region around the moving object optimized toinclude high resolution, the area surrounding the moving objectincluding slightly lower resolution, the top and bottom of the videosegment including significant blur etc.

In some embodiments, the encoder module 210 re-encodes thethree-dimensional video to include optimal segment parameters for eachof the video segments and/or blurs portions of each of the videosegments responsive to a threshold number of people viewing thethree-dimensional video. For example, if only two people viewed thethree-dimensional video, the head-tracking data generated from thosepeople viewing the three-dimensional video may be insufficient toreliably predict a probability of a viewer looking in a particularlocation.

The viewing device 115 may receive the re-encoded video and the optimalsegment parameters for each of the video segments from the encodermodule 210. The viewing device 115 may use the optimal segmentparameters for each of the video segments to un-distort the re-encodedvideo and texture the re-encoded video to the sphere to display there-encoded video with the region of interest for each of the videosegments displayed at a higher resolution that other regions in each ofthe video segments.

In some embodiments, the encoder module 210 re-encodes thethree-dimensional video to include different sets of optimal segmentparameters. For example, the head track module 204 may generate a firstuser profile that reflects a most common region in each of the videosegments and a second user profile that reflects a second most commonregion in each of the video segments. The parameterization module 208may generate first optimal segment parameters associated with the firstuser profile and second optimal segment parameters associated with thesecond user profile. The encoder module 210 may re-encode thethree-dimensional video to include the first optimal segment parametersand the second optimal segment parameters for each of the videosegments. The encoder module 210 may provide the re-encoded video, thefirst optimal segment parameters for each of the video segments, and thesecond optimal segment parameters for each of the video segments to theviewing device 115. The viewing device 115 may un-distort the re-encodedvideo and texture the re-encoded video to the sphere to display there-encoded video with two regions of interest for each of the videosegments displayed at a higher resolution than other regions in each ofthe video segments.

In some embodiments, the head-track module 204 may generate multipleuser profiles where different people were looking at the same region ofinterest for a particular video segment. For example, the head-trackmodule 204 may generate different user profiles based on the age of thepeople that viewed the three-dimensional video. There may be instanceswhere the people in the different age groups looked at the same objectin the three-dimensional video because the object was moving fast,making a loud noise, etc. As a result, in some embodiments, the encodermodule 210 may re-encode the three-dimensional video to include a singleregion of interest at a higher resolution than other regions of interestfor a video segment even though the re-encoded video is based onmultiple sets of segment parameters. In some embodiments where thehead-track module 204 generates a user profile for a particular user,the encoder module 210 may re-encode the three-dimensional video for auser based on the user profile for the particular user.

In some embodiments, the encoder module 210 re-encodes thethree-dimensional video for use as a two-dimensional video. For example,the encoder module 210 re-encodes the three-dimensional video to includethe optimal segment parameters for each of the video segments andprovides a re-encoded video and the optimal segment parameters for eachof the video segments to the client device 105 or the viewing device115. The client device 105 may be used for browser-based players thatdisplay the two-dimensional video, for example, on a computer screen.The viewing device 115 may be used, for example, when a user wants toswitch from an interactive three-dimensional video to an autopilot modethat displays a two-dimensional video that does all the work for theuser.

The client device 105 or the viewing device 115 may use the re-encodedvideo and the optimal segment parameters for each of the video segmentsto generate a two-dimensional video that automates head movement. Theoptimal segment parameters for each video segment provide a model forhow a user moves while watching the three-dimensional video. Thetwo-dimensional video may automate pitch and yaw movements to simulatethe model based on the optimal segment parameters. This mayadvantageously allow users to view an autopilot mode that automates thethree-dimensional movement without having to control the two-dimensionalvideo themselves by using, for example, a mouse, joystick, keys, etc.

In some embodiments, the encoder module 210 generates thetwo-dimensional video from the three-dimensional video based on theoptimal segment parameters. Because the optimal segment parameters for avideo segment indicate a region of interest in the video segment, theencoder module 210 may generate a two-dimensional video that depictshead tracking movement as automatic panning within the two-dimensionalvideo. For example, the encoder module 210 may convert athree-dimensional video that includes a bird flying overhead to atwo-dimensional video where it appears as if the camera moves overheadto look at the bird, the way a person viewing the three-dimensionalvideo would move. This may advantageously allow a person viewing contenton his desktop computer to have a simulated virtual-reality experience.

The encoder module 210 may generate a two-dimensional video from thethree-dimensional video that includes multiple optimal segmentparameters. For example, the encoder module 210 may generate thetwo-dimensional video based on multiple user profiles created based on afirst most common region of interest and a second most common region ofinterest, demographics information, etc.

The encoder module 210 may compress the three-dimensional video togenerate a stream of compressed three-dimensional video data using videocompression techniques. Because portions of the three-dimensional videomay include blurring, the three-dimensional video may be morecompressible than traditional three-dimensional videos. In someimplementations, the aggregation module 202 may encode the stream ofthree-dimensional video data (or compressed three-dimensional videodata) and audio data to form a stream of three-dimensional video. Forexample, the encoder module 210 may compress the stream ofthree-dimensional video data using h.264 and the stream ofthree-dimensional audio data using advanced audio coding (AAC). Inanother example, the encoder module 210 may compress the stream ofthree-dimensional video data and the stream of three-dimensional audiodata using a standard MPEG format.

The user interface module 212 may include code and routines forgenerating a user interface. In some embodiments, the user interfacemodule 212 includes a set of instructions executable by the processor222 to generate the user interface. In some embodiments, the userinterface module 212 is stored in the memory 224 of the computing device200 and is accessible and executable by the processor 222.

In some embodiments, the user interface module 212 may generate a userinterface that includes options for manipulating the camera array 101.For example, the user interface may include options for determiningwhether the camera array 101 starts and stops recording. The userinterface may also include an option for viewing a preview of the videodata captured by the camera array 101.

The user interface module 212 may generate a user interface thatincludes options for viewing the three-dimensional video or atwo-dimensional video generated from the three-dimensional video. Theoptions may include starting and stopping a video. In some embodiments,the user interface includes a timeline of the video and an option toview the video starting at a section on the timeline.

In some embodiments, the user interface module 212 crops the region ofinterest for one or more of the video segments based on the optimalsegment parameters to form one or more thumbnails of one or more croppedregions of interest. For example, the user interface module 212 mayselect a predetermined number of regions of interest to crop in thevideo segments. In some embodiments, the user interface module 212 maydetermine based on the head-tracking data that regions of interest wherea threshold percentage of people looked at the same region of interestthat the region of interest qualifies for cropping. For example, if thethree-dimensional video includes an explosion and 98% of the peoplelooked at the explosion, the user interface module 212 may determinethat the 98% exceeds the threshold percentage of 75% and crop the regionof interest. The user interface module 212 may generate a timeline ofthe three-dimensional video that includes the thumbnails.

Turning to FIG. 3, an example user interface 300 is illustrated thatincludes a video screen 305 for displaying a video and a timeline 310.The video may be a three-dimensional video or a two-dimensional videogenerated from the three-dimensional video. The video screen 305includes a play button 315 for starting the video. The timeline 310includes three thumbnails 320: a first thumbnail 320 a of a zebra, asecond thumbnail 320 b of a peacock, and a third thumbnail 320 c of akilldeer. The thumbnails 320 (320 a, 320 b, 320 c) include a croppedversion of the regions of interest in the video. The user interfacemodule 212 generated the three thumbnails based on a percentage ofpeople viewing the region of interest in each of the video segmentsexceeding a threshold percentage.

In some embodiments, the user interface module 212 generates a userinterface that include an option for the user to modify thetwo-dimensional video. In some embodiments where multiple user profilesare available (e.g., a first user profile and a second user profile),the user interface may include an option to switch from the first userprofile to the second user profile. In this example, the client device105 may switch from using the re-encoded video and first optimal segmentparameters to using the re-encoded video and second optimal segmentparameters to generate the two-dimensional video. In some embodiments,the user interface may provide descriptions for different user profiles,such as most common for the most common regions of interest, second mostcommon, slow movement for head-tracking data associated with people thatmove slowly while viewing the three-dimensional video, fast movement forhead-tracking data associated with people that move quickly whileviewing the three-dimensional video, etc.

In some embodiments, the user interface may include an option for theuser to modify the two-dimensional video by directly inputting a pitchand yaw. For example, where the user is viewing the two-dimensionalvideo on a client device 105, the user may use a pointing device, suchas a mouse, to select a region of interest in the two-dimensional video.The client device 105 may identify the pitch and yaw associated with theregion of interest that the user selected. The client device 105 may usethe pitch and yaw as optimal segment parameters to modify thetwo-dimensional video, for example, by displaying more detail for theselected region of interest. In some embodiments, the client device 105may identify movement associated with the selection. For example, theclient device 105 may identify mouse movement in an upward direction.The client device 105 may, as a result, display the two-dimensionalvideo as panning upwards.

In another example, the user may directly input the pitch and yaw basedon gyroscopic input. For example, if a user is viewing thetwo-dimensional video on a viewing device 115, the user's head may moveupwards. A gyroscope associated with the viewing device 115 may detectthe upward movement and modify the two-dimensional view to display thetwo-dimensional video as panning upwards. In yet another embodiment, theclient device 105 may be a mobile device, such as a smartphone, thatincludes a gyroscope that detects a user rotating the client device 105to simulate upward movement. The client device 105 may modify thetwo-dimensional view to display the two-dimensional video as panningupwards based on the user rotating the client device 105.

Example Flow Diagrams

FIG. 4 illustrates an example flow diagram 400 for generatingthree-dimensional video according to some embodiments. The steps in FIG.4 may be performed by the virtual reality application 103 a stored onthe client device 105, the virtual reality application 103 b stored onthe server 120, or a combination of the virtual reality application 103a stored on the client device 105 and the virtual reality application103 b the server 120.

At step 402, head-tracking data is received that describes one or morepositions of one or more people while the one or more people are viewinga three-dimensional video (3D) on one or more viewing devices 115. Atstep 404, video segments are generated for the three-dimensional video.For example, the three-dimensional video may be divided into videosegments that are each two seconds long. At step 406, for each of thevideo segments, a directional encoding format is determined thatprojects latitudes and longitudes of locations of a surface of a sphereinto locations on a plane, a cost function is determined that identifiesa region of interest on the plane based on the head-tracking data, andoptimal segment parameters are generated that minimize a sum-overposition for the region of interest.

The separation of various components and servers in the embodimentsdescribed herein should not be understood as requiring such separationin all embodiments, and it should be understood that the describedcomponents and servers may generally be integrated together in a singlecomponent or server. Additions, modifications, or omissions may be madeto the illustrated embodiment without departing from the scope of thepresent disclosure, as will be appreciated in view of the disclosure.

FIG. 5 illustrates an example flow diagram 500 for re-encoding athree-dimensional video with blurred portions. The steps in FIG. 5 maybe performed by the virtual reality application 103 a stored on theclient device 105, the virtual reality application 103 b stored on theserver 120, or a combination of the virtual reality application 103 astored on the client device 105 and the virtual reality application 103b the server 120.

At step 502, head-tracking data is received that describes one or morepositions of people while the people are viewing a three-dimensionalvideo on viewing devices 115. At step 504, a probabilistic model of theone or more positions of the people is generated based on thehead-tracking data, where the probabilistic model identifies aprobability of a viewer looking in a particular direction as a functionof time. For example, the probabilistic model may include a heat mapthat includes a visual representation of the probability that a vieweris looking in a particular direction as a function of time.

At step 506, video segments are generated from the three-dimensionalvideo. At step 508, for each of the video segments: determine adirectional encoding format that projects latitudes and longitudes oflocations of a surface of a sphere onto locations on a plane, determinea cost function that identifies a region of interest on the plane basedon the probabilistic model, generate optimal segment parameters thatminimize a sum-over position for the region of interest, and identify aprobability of a viewer looking in a particular direction as a functionof time based on the probabilistic model.

At step 510, the three-dimensional video is re-encoded to include theoptimal segment parameters for each of the video segments and to blurportions of each of the video segments based on the probability, wherean intensity of a level of blur increases as the probability of theviewer looking in the particular direction decreases.

FIG. 6 illustrates an example flow diagram 600 for generating optimalsegment parameters and a probabilistic model from a training set. Thesteps in FIG. 6 may be performed by the virtual reality application 103a stored on the client device 105, the virtual reality application 103 bstored on the server 120, or a combination of the virtual realityapplication 103 a stored on the client device 105 and the virtualreality application 103 b the server 120.

At step 602, head-tracking data is received that describes one or morepositions of people while the people are viewing a set ofthree-dimensional videos. For example, the set of three-dimensionalvideos may include all the three-dimensional videos associated with acompany that produces virtual-reality content. The company may providethe set of three-dimensional videos to users and receive, afterreceiving user consent, the head-track data after the users watch thevirtual reality content.

At step 604, a set of probabilistic models is generated of the one ormore positions of the people based on the head-tracking data. Forexample, the set of probabilistic models describe the probability of aviewer looking in a particular direction for a correspondingthree-dimensional video.

At step 606, a first probabilistic model for a first three-dimensionalvideo is estimated, where the first three-dimensional video is not partof the set of three-dimensional videos. For example, the set ofthree-dimensional videos serve as a training set for a neural networkthat can estimate the probability of a viewer looking in a particulardirection in the first three-dimensional video. This advantageouslyallows the first probabilistic model to be generated without the expenseassociated with having people view the first three-dimensional video togenerate the first probabilistic model.

At step 608, for each of the video segments: a directional encodingformat is determined that projects latitudes and longitudes of locationsof a surface of a sphere onto locations on a plane, a cost function isdetermined that identifies a region of interest on the plane based onthe first probabilistic model, optimal segment parameters are generatedthat minimize a sum-over position for the region of interest, and aprobability is identified of a viewer looking in a particular directionas a function of time based on the first probabilistic model.

Embodiments described herein contemplate various additions,modifications, and/or omissions to the above-described panoptic virtualpresence system, which has been described by way of example only.Accordingly, the above-described camera system should not be construedas limiting. For example, the camera system described with respect toFIG. 1 below may include additional and/or different components orfunctionality than described above without departing from the scope ofthe disclosure.

Embodiments described herein may be implemented using computer-readablemedia for carrying or having computer-executable instructions or datastructures stored thereon. Such computer-readable media may be anyavailable media that may be accessed by a general purpose or specialpurpose computer. By way of example, and not limitation, suchcomputer-readable media may include tangible computer-readable storagemedia including Random Access Memory (RAM), Read-Only Memory (ROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), CompactDisc Read-Only Memory (CD-ROM) or other optical disk storage, magneticdisk storage or other magnetic storage devices, flash memory devices(e.g., solid state memory devices), or any other storage medium whichmay be used to carry or store desired program code in the form ofcomputer-executable instructions or data structures and which may beaccessed by a general purpose or special purpose computer. Combinationsof the above may also be included within the scope of computer-readablemedia.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device (e.g., one or more processors) toperform a certain function or group of functions. Although the subjectmatter has been described in language specific to structural featuresand/or methodological acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as example forms of implementingthe claims.

As used herein, the terms “module” or “component” may refer to specifichardware embodiments configured to perform the operations of the moduleor component and/or software objects or software routines that may bestored on and/or executed by general purpose hardware (e.g.,computer-readable media, processing devices, etc.) of the computingsystem. In some embodiments, the different components, modules, engines,and services described herein may be implemented as objects or processesthat execute on the computing system (e.g., as separate threads). Whilesome of the system and methods described herein are generally describedas being implemented in software (stored on and/or executed by generalpurpose hardware), specific hardware embodiments or a combination ofsoftware and specific hardware embodiments are also possible andcontemplated. In this description, a “computing entity” may be anycomputing system as previously defined herein, or any module orcombination of modulates running on a computing system.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the invention andthe concepts contributed by the inventor to furthering the art, and areto be construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the inventions havebeen described in detail, it may be understood that the various changes,substitutions, and alterations could be made hereto without departingfrom the spirit and scope of the invention.

What is claimed is:
 1. A method comprising: receiving head-tracking datathat describe one or more positions of people while the people areviewing a three-dimensional video; generating a probabilistic model ofthe one or more positions of the people based on the head-tracking data,wherein the probabilistic model identifies a probability of a viewerlooking in a particular direction as a function of time; generatingvideo segments from the three-dimensional video; for each of the videosegments: determining a directional encoding format that projectslatitudes and longitudes of locations of a surface of a sphere ontolocations on a plane; determining a cost function that identifies aregion of interest on the plane based on the probabilistic model; andgenerating optimal segment parameters that minimize a sum-over positionfor the region of interest; and re-encoding the three-dimensional videoto include the optimal segment parameters for each of the video segmentsand to modify portions of each of the video segments based on theprobability of the viewer looking in the particular direction as thefunction of time.
 2. The method of claim 1, further comprisingproviding, based on the re-encoding of the three-dimensional video, are-encoded video and the optimal segment parameters for each of thevideo segments to a viewing device, wherein the viewing device uses theoptimal segment parameters for each of the video segments to un-distortthe re-encoded video and texture the re-encoded video to the sphere todisplay the re-encoded video with the region of interest for each of thevideo segments displayed at a higher resolution than other regions ineach of the video segments.
 3. The method of claim 1, wherein there-encoding of the three-dimensional is performed in response to athreshold number of the people viewing the three-dimensional video. 4.The method of claim 1, wherein the re-encoding of the three-dimensionalvideo includes modifying portions of each of the video segments byblurring portions of each of the video segments based on theprobability.
 5. The method of claim 4, wherein an intensity of a levelof blur increases as the probability of the viewer looking in theparticular direction decreases.
 6. The method of claim 4, furthercomprising identifying a region of low interest for each of the videosegments, wherein: the blurring of the portions of each of the videosegments includes blurring the region of low interest; and the region oflow interest is displayed at a lower resolution that other regions ineach of the video segments.
 7. The method of claim 1, further comprisingproviding the re-encoded video and the optimal segment parameters foreach of the video segments to a client device, wherein the client deviceuses the re-encoded video and the optimal segment parameters for each ofthe video segments to generate a two-dimensional video that automateshead movement.
 8. The method of claim 1, wherein the probabilistic modelis a heat map.
 9. A system comprising: a processor coupled to a memory;a head tracking module stored in the memory and executable by theprocessor, the head tracking module configured to receive head-trackingdata that describe one or more positions of people while the people areviewing a set of three-dimensional videos, generate a set ofprobabilistic models of the one or more positions of the people based onthe head-tracking data, and estimate a first probabilistic model for athree-dimensional video, wherein the first probabilistic modelidentifies a probability of a viewer looking in a particular directionas a function of time and the three-dimensional video is not part of theset of three-dimensional videos; a segmentation module stored in thememory and executable by the processor, the segmentation moduleconfigured to generate video segments from the three-dimensional video;a parameterization module stored in the memory and executable by theprocessor, the parameterization module configured to, for each of thevideo segments: determine a directional encoding format that projectslatitudes and longitudes of locations of a surface of a sphere ontolocations on a plane; determine a cost function that identifies a regionof interest on the plane based on the first probabilistic model; andgenerate optimal segment parameters that minimize a sum-over positionfor the region of interest; and an encoder module stored in the memoryand executable by the processor, the encoder module configured tore-encode the three-dimensional video to include the optimal segmentparameters for each of the video segments and modify of each of thevideo segments based on the probability of the viewer looking in theparticular direction as the function of time.
 10. The system of claim 9,wherein the encoder module is further configured to provide, based onthe re-encoding of the three-dimensional video, a re-encoded video andthe optimal segment parameters for each of the video segments to aclient device, wherein the client device uses the re-encoded video andthe optimal segment parameters for each of the video segments togenerate a two-dimensional video that automates head movement.
 11. Thesystem of claim 9, wherein the first probabilistic model is a heat map.12. The system of claim 9, wherein the re-encoding of thethree-dimensional video includes modifying portions of each of the videosegments by blurring portions of each of the video segments based on theprobability.
 13. The system of claim 12, wherein the encoder module isfurther configured to provide a recommendation of where to look byblurring every portion of the re-encoded video except for a location inthe re-encoded video where a viewer is recommended to look.
 14. Thesystem of claim 12, wherein the re-encoding of the three-dimensional isperformed in response to a threshold number of the people viewing thethree-dimensional video.
 15. The system of claim 12, further comprisinga user interface module stored in the memory and executable by theprocessor, the user interface module configured to crop the region ofinterest for one or more video segments based on the optimal segmentparameters to form one or more thumbnails of one or more cropped regionsof interest and generate a timeline of the three-dimensional video withthe one or more thumbnails.
 16. A non-transitory computer readablestorage medium storing instructions that, when executed by a processor,cause the processor to: receive head-tracking data that describes one ormore positions of people while the people are viewing athree-dimensional video; generate a probabilistic model of the one ormore positions of the people based on the head-tracking data, whereinthe probabilistic model identifies a probability of a viewer looking ina particular direction as a function of time; generate video segmentsfrom the three-dimensional video; for each of the video segments:determine a directional encoding format that projects latitudes andlongitudes of locations of a surface of a sphere onto locations on aplane; determine a cost function that identifies a region of interest onthe plane based on the probabilistic model; generate optimal segmentparameters that minimize a sum-over position for the region of interest;and identify a region of low interest; and re-encode thethree-dimensional video to include the optimal segment parameters foreach of the video segments and to modify the region of low interestbased on the probability of the viewer looking in the particulardirection as the function of time.
 17. The non-transitory computerreadable storage medium of claim 16, wherein the processor is furtherconfigured to provide, based on the re-encoding of the three-dimensionalvideo, a re-encoded video and the optimal segment parameters for each ofthe video segments to a viewing device, wherein the viewing device usesthe optimal segment parameters for each of the video segments toun-distort the re-encoded video and texture the re-encoded video to thesphere to display the re-encoded video with the region of interest foreach of the video segments displayed at a higher resolution than otherregions in each of the video segments and the region of low interestdisplayed at a lower resolution than other regions in each of the videosegments.
 18. The non-transitory computer readable storage medium ofclaim 16, wherein the probabilistic model is a heat map.
 19. Thenon-transitory computer readable storage medium of claim 16, wherein there-encoding of the three-dimensional video includes modifying portionsof each of the video segments by blurring portions of each of the videosegments based on the probability.
 20. The non-transitory computerreadable storage medium of claim 19, wherein the re-encoding of thethree-dimensional is performed in response to a threshold number of thepeople viewing the three-dimensional video.